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pyscf/pyscf | 0adfb464333f5ceee07b664f291d4084801bae64 | pyscf/gto/mole.py | python | Mole.ao2mo | (self, mo_coeffs, erifile=None, dataname='eri_mo', intor='int2e',
**kwargs) | return ao2mo.kernel(self, mo_coeffs, erifile, dataname, intor, **kwargs) | Integral transformation for arbitrary orbitals and arbitrary
integrals. See more detalied documentation in func:`ao2mo.kernel`.
Args:
mo_coeffs (an np array or a list of arrays) : A matrix of orbital
coefficients if it is a numpy ndarray, or four sets of orbital
coefficients, corresponding to the four indices of (ij|kl).
Kwargs:
erifile (str or h5py File or h5py Group object) : The file/object
to store the transformed integrals. If not given, the return
value is an array (in memory) of the transformed integrals.
dataname : str
*Note* this argument is effective if erifile is given.
The dataset name in the erifile (ref the hierarchy of HDF5 format
http://www.hdfgroup.org/HDF5/doc1.6/UG/09_Groups.html). By assigning
different dataname, the existed integral file can be reused. If
the erifile contains the specified dataname, the old integrals
will be replaced by the new one under the key dataname.
intor (str) : integral name Name of the 2-electron integral. Ref
to :func:`getints_by_shell`
for the complete list of available 2-electron integral names
Returns:
An array of transformed integrals if erifile is not given.
Otherwise, return the file/fileobject if erifile is assigned.
Examples:
>>> import pyscf
>>> mol = pyscf.M(atom='O 0 0 0; H 0 1 0; H 0 0 1', basis='sto3g')
>>> mo1 = numpy.random.random((mol.nao_nr(), 10))
>>> mo2 = numpy.random.random((mol.nao_nr(), 8))
>>> eri1 = mol.ao2mo(mo1)
>>> print(eri1.shape)
(55, 55)
>>> eri1 = mol.ao2mo(mo1, compact=False)
>>> print(eri1.shape)
(100, 100)
>>> eri1 = mol.ao2mo(eri, (mo1,mo2,mo2,mo2))
>>> print(eri1.shape)
(80, 36)
>>> eri1 = mol.ao2mo(eri, (mo1,mo2,mo2,mo2), erifile='water.h5') | Integral transformation for arbitrary orbitals and arbitrary
integrals. See more detalied documentation in func:`ao2mo.kernel`. | [
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] | def ao2mo(self, mo_coeffs, erifile=None, dataname='eri_mo', intor='int2e',
**kwargs):
'''Integral transformation for arbitrary orbitals and arbitrary
integrals. See more detalied documentation in func:`ao2mo.kernel`.
Args:
mo_coeffs (an np array or a list of arrays) : A matrix of orbital
coefficients if it is a numpy ndarray, or four sets of orbital
coefficients, corresponding to the four indices of (ij|kl).
Kwargs:
erifile (str or h5py File or h5py Group object) : The file/object
to store the transformed integrals. If not given, the return
value is an array (in memory) of the transformed integrals.
dataname : str
*Note* this argument is effective if erifile is given.
The dataset name in the erifile (ref the hierarchy of HDF5 format
http://www.hdfgroup.org/HDF5/doc1.6/UG/09_Groups.html). By assigning
different dataname, the existed integral file can be reused. If
the erifile contains the specified dataname, the old integrals
will be replaced by the new one under the key dataname.
intor (str) : integral name Name of the 2-electron integral. Ref
to :func:`getints_by_shell`
for the complete list of available 2-electron integral names
Returns:
An array of transformed integrals if erifile is not given.
Otherwise, return the file/fileobject if erifile is assigned.
Examples:
>>> import pyscf
>>> mol = pyscf.M(atom='O 0 0 0; H 0 1 0; H 0 0 1', basis='sto3g')
>>> mo1 = numpy.random.random((mol.nao_nr(), 10))
>>> mo2 = numpy.random.random((mol.nao_nr(), 8))
>>> eri1 = mol.ao2mo(mo1)
>>> print(eri1.shape)
(55, 55)
>>> eri1 = mol.ao2mo(mo1, compact=False)
>>> print(eri1.shape)
(100, 100)
>>> eri1 = mol.ao2mo(eri, (mo1,mo2,mo2,mo2))
>>> print(eri1.shape)
(80, 36)
>>> eri1 = mol.ao2mo(eri, (mo1,mo2,mo2,mo2), erifile='water.h5')
'''
from pyscf import ao2mo
return ao2mo.kernel(self, mo_coeffs, erifile, dataname, intor, **kwargs) | [
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|
pm4py/pm4py-core | 7807b09a088b02199cd0149d724d0e28793971bf | pm4py/algo/organizational_mining/sna/util.py | python | cluster_affinity_propagation | (sna_results: List[List[Any]], parameters=None) | return ret | Performs a clustering using the affinity propagation algorithm provided by Scikit Learn
Parameters
--------------
sna_results
Values for a SNA metric
parameters
Parameters of the algorithm
Returns
--------------
clustering
Dictionary that contains, for each cluster that has been identified,
the list of resources of the cluster | Performs a clustering using the affinity propagation algorithm provided by Scikit Learn | [
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"provided",
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] | def cluster_affinity_propagation(sna_results: List[List[Any]], parameters=None) -> Dict[str, List[str]]:
"""
Performs a clustering using the affinity propagation algorithm provided by Scikit Learn
Parameters
--------------
sna_results
Values for a SNA metric
parameters
Parameters of the algorithm
Returns
--------------
clustering
Dictionary that contains, for each cluster that has been identified,
the list of resources of the cluster
"""
from sklearn.cluster import AffinityPropagation
if parameters is None:
parameters = {}
matrix = sna_results[0]
originators = sna_results[1]
affinity_propagation = AffinityPropagation(**parameters)
affinity_propagation.fit(matrix)
clusters = affinity_propagation.predict(matrix)
ret = {}
for i in range(len(clusters)):
res = originators[i]
cluster = str(clusters[i])
if cluster not in ret:
ret[cluster] = []
ret[cluster].append(res)
return ret | [
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|
turicas/brasil.io | f1c371fe828a090510259a5027b49e2e651936b4 | utils/conversion.py | python | csv2sqlite | (
input_filename,
output_filename,
table_name,
samples=30000,
batch_size=10000,
encoding="utf-8",
callback=None,
force_types=None,
) | [] | def csv2sqlite(
input_filename,
output_filename,
table_name,
samples=30000,
batch_size=10000,
encoding="utf-8",
callback=None,
force_types=None,
):
# Identify data types
fobj = open_compressed(input_filename, encoding)
reader = csv.reader(fobj)
header = next(reader)
data = []
for index, row in enumerate(reader):
row = dict(zip(header, row))
if index == samples:
break
data.append(row)
fields = rows.import_from_dicts(data, import_fields=header).fields
if force_types is not None:
fields.update(force_types)
# Create lazy table object to be converted
table = rows.Table(fields=fields)
reader = csv.reader(open_compressed(input_filename, encoding))
next(reader) # skip header
table._rows = reader
# Export to SQLite
rows.export_to_sqlite(table, output_filename, table_name=table_name, callback=callback, batch_size=batch_size) | [
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||||
hydroshare/hydroshare | 7ba563b55412f283047fb3ef6da367d41dec58c6 | hs_app_netCDF/serialization.py | python | NetcdfResourceMeta._read_resource_metadata | (self) | [] | def _read_resource_metadata(self):
super(NetcdfResourceMeta, self)._read_resource_metadata()
print("--- NetcdfResourceMeta ---")
# Also parse using SAX so that we can capture certain metadata elements
# in the same order in which they appear in the RDF+XML serialization.
SAX_parse_results = NetcdfResourceSAXHandler()
xml.sax.parse(self.rmeta_path, SAX_parse_results)
hsterms = rdflib.namespace.Namespace('https://www.hydroshare.org/terms/')
# Get Variable
if SAX_parse_results:
# Use variables from SAX parser
self.variables = list(SAX_parse_results.variables)
else:
for s, p, o in self._rmeta_graph.triples((None, hsterms.netcdfVariable, None)):
var = NetcdfResourceMeta.Variable()
# Get name
name_lit = self._rmeta_graph.value(o, hsterms.name)
if name_lit is None:
msg = "Name for Variable was not found for resource {0}".format(self.root_uri)
raise GenericResourceMeta.ResourceMetaException(msg)
var.name = str(name_lit)
# Get shape
shape_lit = self._rmeta_graph.value(o, hsterms.shape)
if shape_lit is None:
msg = "Shape for Variable was not found for resource {0}".format(self.root_uri)
raise GenericResourceMeta.ResourceMetaException(msg)
var.shape = str(shape_lit)
# Get type
type_lit = self._rmeta_graph.value(o, hsterms.type)
if type_lit is None:
msg = "Type for Variable was not found for resource {0}".format(self.root_uri)
raise GenericResourceMeta.ResourceMetaException(msg)
var.type = str(type_lit)
# Get unit
unit_lit = self._rmeta_graph.value(o, hsterms.unit)
if unit_lit is None:
msg = "Unit for Variable was not found for resource {0}".format(self.root_uri)
raise GenericResourceMeta.ResourceMetaException(msg)
var.unit = str(unit_lit)
# Get longName
long_name_lit = self._rmeta_graph.value(o, hsterms.longName)
if long_name_lit:
var.longName = str(long_name_lit)
# Get comment
comment_lit = self._rmeta_graph.value(o, hsterms.comment)
if comment_lit:
var.comment = str(comment_lit)
# Get missingValue
missing_val_lit = self._rmeta_graph.value(o, hsterms.missingValue)
if missing_val_lit:
var.missingValue = str(missing_val_lit)
self.variables.append(var)
for v in self.variables:
print("\t\t{0}".format(str(v)))
# Get spatialReference
for s, p, o in self._rmeta_graph.triples((None, hsterms.spatialReference, None)):
# Get extent
extent_lit = self._rmeta_graph.value(o, hsterms.extent)
if extent_lit is None:
msg = "Extent not found in spatial reference for resource {0}".format(self.root_uri)
raise GenericResourceMeta.ResourceMetaException(msg)
extent = str(extent_lit)
# Get crsName
crs_name_lit = self._rmeta_graph.value(o, hsterms.crsName)
crs_name = None
if crs_name_lit is not None:
crs_name = str(crs_name_lit)
# Get crsRepresentationText
crs_repr_text_lit = self._rmeta_graph.value(o, hsterms.crsRepresentationText)
crs_repr_text = None
if crs_repr_text_lit is not None:
crs_repr_text = str(crs_repr_text_lit)
# Get crsRepresentationType
crs_repr_type_lit = self._rmeta_graph.value(o, hsterms.crsRepresentationType)
crs_repr_type = None
if crs_repr_type_lit is not None:
crs_repr_type = str(crs_repr_type_lit)
self.spatial_reference = NetcdfResourceMeta.SpatialReference(extent, crs_name, crs_repr_text,
crs_repr_type)
print("\t\t{0}".format(self.spatial_reference)) | [
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||||
Pymol-Scripts/Pymol-script-repo | bcd7bb7812dc6db1595953dfa4471fa15fb68c77 | modules/pdb2pqr/contrib/numpy-1.1.0/numpy/oldnumeric/ma.py | python | resize | (a, new_shape) | return result | resize(a, new_shape) returns a new array with the specified shape.
The original array's total size can be any size. | resize(a, new_shape) returns a new array with the specified shape.
The original array's total size can be any size. | [
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"""resize(a, new_shape) returns a new array with the specified shape.
The original array's total size can be any size."""
m = getmask(a)
if m is not nomask:
m = fromnumeric.resize(m, new_shape)
result = array(fromnumeric.resize(filled(a), new_shape), mask=m)
result.set_fill_value(get_fill_value(a))
return result | [
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|
9miao/G-Firefly | 8fbeeb3ef9782600560be48228c91cfb8f5ff87d | gfirefly/gfirefly/distributed/root.py | python | PBRoot.callChildNotForResult | (self,childname,*args,**kw) | 调用子节点的接口
@param childId: int 子节点的id
return Defered Object | 调用子节点的接口 | [
"调用子节点的接口"
] | def callChildNotForResult(self,childname,*args,**kw):
'''调用子节点的接口
@param childId: int 子节点的id
return Defered Object
'''
self.childsmanager.callChildNotForResult(childname,*args,**kw) | [
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biolab/orange3 | 41685e1c7b1d1babe680113685a2d44bcc9fec0b | Orange/clustering/hierarchical.py | python | feature_clustering | (data, distance=PearsonR,
linkage=AVERAGE) | return dist_matrix_clustering(matrix, linkage=linkage) | Return the hierarchical clustering of the dataset's columns.
:param Orange.data.Table data: Dataset to cluster.
:param Orange.distance.Distance distance: A distance measure.
:param str linkage: | Return the hierarchical clustering of the dataset's columns. | [
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] | def feature_clustering(data, distance=PearsonR,
linkage=AVERAGE):
"""
Return the hierarchical clustering of the dataset's columns.
:param Orange.data.Table data: Dataset to cluster.
:param Orange.distance.Distance distance: A distance measure.
:param str linkage:
"""
matrix = distance(data, axis=0)
return dist_matrix_clustering(matrix, linkage=linkage) | [
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|
sefakilic/goodreads | 5187100d66b87c87db8b095bbf576e1b92422a23 | goodreads/client.py | python | GoodreadsClient.find_author | (self, author_name) | return self.author(resp['author']['@id']) if 'author' in resp else None | Find an author by name | Find an author by name | [
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"""Find an author by name"""
resp = self.request("api/author_url/%s" % author_name, {})
return self.author(resp['author']['@id']) if 'author' in resp else None | [
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|
AppScale/gts | 46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9 | AdminServer/appscale/admin/utils.py | python | remove_old_archives | (project_id, service_id, version) | Cleans up old revision archives.
Args:
project_id: A string specifying a project ID.
service_id: A string specifying a service ID.
version: A dictionary containing version details. | Cleans up old revision archives. | [
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] | def remove_old_archives(project_id, service_id, version):
""" Cleans up old revision archives.
Args:
project_id: A string specifying a project ID.
service_id: A string specifying a service ID.
version: A dictionary containing version details.
"""
prefix = VERSION_PATH_SEPARATOR.join(
[project_id, service_id, version['id']])
current_name = os.path.basename(version['deployment']['zip']['sourceUrl'])
old_sources = [os.path.join(SOURCES_DIRECTORY, archive) for archive
in os.listdir(SOURCES_DIRECTORY)
if archive.startswith(prefix) and archive < current_name]
for archive in old_sources:
os.remove(archive) | [
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||
oracle/graalpython | 577e02da9755d916056184ec441c26e00b70145c | graalpython/lib-python/3/encodings/iso8859_15.py | python | IncrementalEncoder.encode | (self, input, final=False) | return codecs.charmap_encode(input,self.errors,encoding_table)[0] | [] | def encode(self, input, final=False):
return codecs.charmap_encode(input,self.errors,encoding_table)[0] | [
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|||
pymedusa/Medusa | 1405fbb6eb8ef4d20fcca24c32ddca52b11f0f38 | ext/tmdbsimple/movies.py | python | Keywords.info | (self, **kwargs) | return response | Get the details of a keyword.
Args:
None
Returns:
A dict representation of the JSON returned from the API. | Get the details of a keyword. | [
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"""
Get the details of a keyword.
Args:
None
Returns:
A dict representation of the JSON returned from the API.
"""
path = self._get_id_path('info')
response = self._GET(path, kwargs)
self._set_attrs_to_values(response)
return response | [
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|
wasidennis/AdaptSegNet | fca9ff0f09dab45d44bf6d26091377ac66607028 | evaluate_cityscapes.py | python | main | () | Create the model and start the evaluation process. | Create the model and start the evaluation process. | [
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"""Create the model and start the evaluation process."""
args = get_arguments()
gpu0 = args.gpu
if not os.path.exists(args.save):
os.makedirs(args.save)
if args.model == 'DeeplabMulti':
model = DeeplabMulti(num_classes=args.num_classes)
elif args.model == 'Oracle':
model = Res_Deeplab(num_classes=args.num_classes)
if args.restore_from == RESTORE_FROM:
args.restore_from = RESTORE_FROM_ORC
elif args.model == 'DeeplabVGG':
model = DeeplabVGG(num_classes=args.num_classes)
if args.restore_from == RESTORE_FROM:
args.restore_from = RESTORE_FROM_VGG
if args.restore_from[:4] == 'http' :
saved_state_dict = model_zoo.load_url(args.restore_from)
else:
saved_state_dict = torch.load(args.restore_from)
### for running different versions of pytorch
model_dict = model.state_dict()
saved_state_dict = {k: v for k, v in saved_state_dict.items() if k in model_dict}
model_dict.update(saved_state_dict)
###
model.load_state_dict(saved_state_dict)
model.eval()
model.cuda(gpu0)
testloader = data.DataLoader(cityscapesDataSet(args.data_dir, args.data_list, crop_size=(1024, 512), mean=IMG_MEAN, scale=False, mirror=False, set=args.set),
batch_size=1, shuffle=False, pin_memory=True)
if version.parse(torch.__version__) >= version.parse('0.4.0'):
interp = nn.Upsample(size=(1024, 2048), mode='bilinear', align_corners=True)
else:
interp = nn.Upsample(size=(1024, 2048), mode='bilinear')
for index, batch in enumerate(testloader):
if index % 100 == 0:
print '%d processd' % index
image, _, name = batch
if args.model == 'DeeplabMulti':
output1, output2 = model(Variable(image, volatile=True).cuda(gpu0))
output = interp(output2).cpu().data[0].numpy()
elif args.model == 'DeeplabVGG' or args.model == 'Oracle':
output = model(Variable(image, volatile=True).cuda(gpu0))
output = interp(output).cpu().data[0].numpy()
output = output.transpose(1,2,0)
output = np.asarray(np.argmax(output, axis=2), dtype=np.uint8)
output_col = colorize_mask(output)
output = Image.fromarray(output)
name = name[0].split('/')[-1]
output.save('%s/%s' % (args.save, name))
output_col.save('%s/%s_color.png' % (args.save, name.split('.')[0])) | [
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home-assistant/core | 265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1 | homeassistant/util/yaml/loader.py | python | Secrets._load_secret_yaml | (self, secret_dir: Path) | return secrets | Load the secrets yaml from path. | Load the secrets yaml from path. | [
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"""Load the secrets yaml from path."""
if (secret_path := secret_dir / SECRET_YAML) in self._cache:
return self._cache[secret_path]
_LOGGER.debug("Loading %s", secret_path)
try:
secrets = load_yaml(str(secret_path))
if not isinstance(secrets, dict):
raise HomeAssistantError("Secrets is not a dictionary")
if "logger" in secrets:
logger = str(secrets["logger"]).lower()
if logger == "debug":
_LOGGER.setLevel(logging.DEBUG)
else:
_LOGGER.error(
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logger,
)
del secrets["logger"]
except FileNotFoundError:
secrets = {}
self._cache[secret_path] = secrets
return secrets | [
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securesystemslab/zippy | ff0e84ac99442c2c55fe1d285332cfd4e185e089 | zippy/benchmarks/src/benchmarks/sympy/sympy/physics/quantum/boson.py | python | BosonOp._eval_commutator_BosonOp | (self, other, **hints) | return None | [] | def _eval_commutator_BosonOp(self, other, **hints):
if self.name == other.name:
# [a^\dagger, a] = -1
if not self.is_annihilation and other.is_annihilation:
return Integer(-1)
elif 'independent' in hints and hints['independent']:
# [a, b] = 0
return Integer(0)
return None | [
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translate/translate | 72816df696b5263abfe80ab59129b299b85ae749 | translate/storage/html.py | python | htmlfile.handle_charref | (self, name) | Handle entries in the form &#NNNN; e.g. ⃡ | Handle entries in the form &#NNNN; e.g. ⃡ | [
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if name.lower().startswith("x"):
self.handle_data(chr(int(name[1:], 16)))
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qutebrowser/qutebrowser | 3a2aaaacbf97f4bf0c72463f3da94ed2822a5442 | qutebrowser/api/cmdutils.py | python | check_exclusive | (flags: Iterable[bool], names: Iterable[str]) | Check if only one flag is set with exclusive flags.
Raise a CommandError if not.
Args:
flags: The flag values to check.
names: A list of names (corresponding to the flags argument). | Check if only one flag is set with exclusive flags. | [
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"""Check if only one flag is set with exclusive flags.
Raise a CommandError if not.
Args:
flags: The flag values to check.
names: A list of names (corresponding to the flags argument).
"""
if sum(1 for e in flags if e) > 1:
argstr = '/'.join('-' + e for e in names)
raise CommandError("Only one of {} can be given!".format(argstr)) | [
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FederatedAI/FATE | 32540492623568ecd1afcb367360133616e02fa3 | python/federatedml/ensemble/basic_algorithms/decision_tree/tree_core/g_h_optim.py | python | GHPacker.fixedpoint_encode | (num, mul) | return int_fixpoint | [] | def fixedpoint_encode(num, mul):
int_fixpoint = int(round(num * mul))
return int_fixpoint | [
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|||
cloudera/impyla | 0c736af4cad2bade9b8e313badc08ec50e81c948 | impala/_thrift_gen/hive_metastore/ThriftHiveMetastore.py | python | get_function_result.write | (self, oprot) | [] | def write(self, oprot):
if oprot._fast_encode is not None and self.thrift_spec is not None:
oprot.trans.write(oprot._fast_encode(self, [self.__class__, self.thrift_spec]))
return
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self.o2.write(oprot)
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f-dangel/backpack | 1da7e53ebb2c490e2b7dd9f79116583641f3cca1 | backpack/extensions/secondorder/sqrt_ggn/dropout.py | python | SqrtGGNDropout.__init__ | (self) | Pass derivatives for ``torch.nn.Dropout`` module. | Pass derivatives for ``torch.nn.Dropout`` module. | [
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] | def __init__(self):
"""Pass derivatives for ``torch.nn.Dropout`` module."""
super().__init__(DropoutDerivatives()) | [
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Squarespace/pgbedrock | 58e46f98da72e81ae0c2f69463aa6e97ea7b8936 | pgbedrock/privileges.py | python | PrivilegeAnalyzer.analyze_defaults | (self) | Analyze default privileges. Note that we sort the grants / revokes before issuing
them so the output will be more organized, making it easier for the end user to read | Analyze default privileges. Note that we sort the grants / revokes before issuing
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defaults_to_grant = self.desired_defaults.difference(self.current_defaults)
logger.debug('defaults_to_grant: {}'.format(defaults_to_grant))
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self.revoke_default(grantor, schema, pg_priv_kind) | [
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PetterKraabol/Twitch-Chat-Downloader | 7d8b00d1836cbb804489a75b57d6af131fc2cc55 | tcd/logger.py | python | Logger.log | (self, message: str = '', log_type: str = Log.REGULAR, retain: bool = True) | return log | Log a message
:param message: Log message
:param log_type: Log type
:param retain: Save log to memory
:return: None | Log a message
:param message: Log message
:param log_type: Log type
:param retain: Save log to memory
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"""
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:param log_type: Log type
:param retain: Save log to memory
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"""
# Add log to
log = Log(message, log_type)
# Save log entry to memory
if retain and log.type is not Log.PREVIEW:
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# Save log when debugging
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Tautulli/Tautulli | 2410eb33805aaac4bd1c5dad0f71e4f15afaf742 | lib/dns/resolver.py | python | Cache.put | (self, key, value) | Associate key and value in the cache.
*key*, a ``(dns.name.Name, int, int)`` tuple whose values are the
query name, rdtype, and rdclass respectively.
*value*, a ``dns.resolver.Answer``, the answer. | Associate key and value in the cache. | [
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"""Associate key and value in the cache.
*key*, a ``(dns.name.Name, int, int)`` tuple whose values are the
query name, rdtype, and rdclass respectively.
*value*, a ``dns.resolver.Answer``, the answer.
"""
with self.lock:
self._maybe_clean()
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dimagi/commcare-hq | d67ff1d3b4c51fa050c19e60c3253a79d3452a39 | corehq/apps/locations/management/commands/fix_loc_type_reference.py | python | has_bad_location_types | (domain) | return (SQLLocation.objects
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|||
jd/tenacity | 005ef22015ec4fda8646bf9dce7aaa1a4fa121d2 | tenacity/__init__.py | python | retry | (*dargs: t.Any, **dkw: t.Any) | Wrap a function with a new `Retrying` object.
:param dargs: positional arguments passed to Retrying object
:param dkw: keyword arguments passed to the Retrying object | Wrap a function with a new `Retrying` object. | [
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] | def retry(*dargs: t.Any, **dkw: t.Any) -> t.Union[WrappedFn, t.Callable[[WrappedFn], WrappedFn]]: # noqa
"""Wrap a function with a new `Retrying` object.
:param dargs: positional arguments passed to Retrying object
:param dkw: keyword arguments passed to the Retrying object
"""
# support both @retry and @retry() as valid syntax
if len(dargs) == 1 and callable(dargs[0]):
return retry()(dargs[0])
else:
def wrap(f: WrappedFn) -> WrappedFn:
if isinstance(f, retry_base):
warnings.warn(
f"Got retry_base instance ({f.__class__.__name__}) as callable argument, "
f"this will probably hang indefinitely (did you mean retry={f.__class__.__name__}(...)?)"
)
if iscoroutinefunction(f):
r: "BaseRetrying" = AsyncRetrying(*dargs, **dkw)
elif tornado and hasattr(tornado.gen, "is_coroutine_function") and tornado.gen.is_coroutine_function(f):
r = TornadoRetrying(*dargs, **dkw)
else:
r = Retrying(*dargs, **dkw)
return r.wraps(f)
return wrap | [
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||
MacHu-GWU/uszipcode-project | d5ca6d7bd0544043dfc8fee3393ee17e1c96c01d | uszipcode/search.py | python | SearchEngine.by_land_area_in_sqmi | (
self,
lower: int = -1,
upper: int = 2 ** 31,
zipcode_type: ZipcodeTypeEnum = ZipcodeTypeEnum.Standard,
sort_by: str = SimpleZipcode.land_area_in_sqmi.name,
ascending: bool = False,
returns: int = DEFAULT_LIMIT,
) | return self.query(
land_area_in_sqmi_lower=lower,
land_area_in_sqmi_upper=upper,
sort_by=sort_by, zipcode_type=zipcode_type,
ascending=ascending, returns=returns,
) | Search zipcode information by land area / sq miles range. | Search zipcode information by land area / sq miles range. | [
"Search",
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"by",
"land",
"area",
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"sq",
"miles",
"range",
"."
] | def by_land_area_in_sqmi(
self,
lower: int = -1,
upper: int = 2 ** 31,
zipcode_type: ZipcodeTypeEnum = ZipcodeTypeEnum.Standard,
sort_by: str = SimpleZipcode.land_area_in_sqmi.name,
ascending: bool = False,
returns: int = DEFAULT_LIMIT,
):
"""
Search zipcode information by land area / sq miles range.
"""
return self.query(
land_area_in_sqmi_lower=lower,
land_area_in_sqmi_upper=upper,
sort_by=sort_by, zipcode_type=zipcode_type,
ascending=ascending, returns=returns,
) | [
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|
ros/ros | 93d8da32091b8b43702eab5d3202f4511dfeb7dc | tools/rosunit/src/rosunit/pmon.py | python | shutdown_process_monitor | (process_monitor) | @param process_monitor: process monitor to kill
@type process_monitor: L{ProcessMonitor}
@return: True if process_monitor was successfully
shutdown. False if it could not be shutdown cleanly or if there is
a problem with process_monitor
parameter. shutdown_process_monitor() does not throw any exceptions
as this is shutdown-critical code.
@rtype: bool | [] | def shutdown_process_monitor(process_monitor):
"""
@param process_monitor: process monitor to kill
@type process_monitor: L{ProcessMonitor}
@return: True if process_monitor was successfully
shutdown. False if it could not be shutdown cleanly or if there is
a problem with process_monitor
parameter. shutdown_process_monitor() does not throw any exceptions
as this is shutdown-critical code.
@rtype: bool
"""
try:
if process_monitor is None or process_monitor.is_shutdown:
return False
process_monitor.shutdown()
process_monitor.join(20.0)
if process_monitor.isAlive():
return False
else:
return True
except Exception:
return False | [
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|||
simetenn/uncertainpy | ffb2400289743066265b9a8561cdf3b72e478a28 | src/uncertainpy/features/efel_features.py | python | EfelFeatures.reference_feature | (self, time, values, info) | return time, values | An example of an Efel feature. Efel feature functions have the following
requirements, and the given parameters must either be returned by
``model.run`` or ``features.preprocess``.
Parameters
----------
time : {None, numpy.nan, array_like}
Time values of the model. If no time values it is None or numpy.nan.
values : array_like
Result of the model.
info : dictionary
A dictionary with info["stimulus_start"] and info["stimulus_end"]
set.
Returns
-------
time : None
No mean Efel feature has time values, so None is returned instead.
values : array_like
The feature results, `values`. Returns None if there are no feature
results and that evaluation are disregarded.
See also
--------
uncertainpy.features.Features.preprocess : The features preprocess method.
uncertainpy.models.Model.run : The model run method | An example of an Efel feature. Efel feature functions have the following
requirements, and the given parameters must either be returned by
``model.run`` or ``features.preprocess``. | [
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"""
An example of an Efel feature. Efel feature functions have the following
requirements, and the given parameters must either be returned by
``model.run`` or ``features.preprocess``.
Parameters
----------
time : {None, numpy.nan, array_like}
Time values of the model. If no time values it is None or numpy.nan.
values : array_like
Result of the model.
info : dictionary
A dictionary with info["stimulus_start"] and info["stimulus_end"]
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values : array_like
The feature results, `values`. Returns None if there are no feature
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See also
--------
uncertainpy.features.Features.preprocess : The features preprocess method.
uncertainpy.models.Model.run : The model run method
"""
# Perform feature calculations here
time = None
values = None
return time, values | [
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|
microsoft/MPNet | 081523a788c1556f28dd90cbc629810f48b083fb | pretraining/fairseq/optim/bmuf.py | python | FairseqBMUF.step | (self, closure=None) | Performs a single optimization step. | Performs a single optimization step. | [
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if self._is_warmup_end():
self._warmup_sync()
elif self._is_bmuf_iter():
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||
general03/flask-autoindex | 424246242c9f40aeb9ac2c8c63f4d2234024256e | .eggs/Jinja2-3.0.0a1-py3.7.egg/jinja2/environment.py | python | Environment.overlay | (
self,
block_start_string=missing,
block_end_string=missing,
variable_start_string=missing,
variable_end_string=missing,
comment_start_string=missing,
comment_end_string=missing,
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trim_blocks=missing,
lstrip_blocks=missing,
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optimized=missing,
undefined=missing,
finalize=missing,
autoescape=missing,
loader=missing,
cache_size=missing,
auto_reload=missing,
bytecode_cache=missing,
) | return _environment_sanity_check(rv) | Create a new overlay environment that shares all the data with the
current environment except for cache and the overridden attributes.
Extensions cannot be removed for an overlayed environment. An overlayed
environment automatically gets all the extensions of the environment it
is linked to plus optional extra extensions.
Creating overlays should happen after the initial environment was set
up completely. Not all attributes are truly linked, some are just
copied over so modifications on the original environment may not shine
through. | Create a new overlay environment that shares all the data with the
current environment except for cache and the overridden attributes.
Extensions cannot be removed for an overlayed environment. An overlayed
environment automatically gets all the extensions of the environment it
is linked to plus optional extra extensions. | [
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comment_start_string=missing,
comment_end_string=missing,
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undefined=missing,
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autoescape=missing,
loader=missing,
cache_size=missing,
auto_reload=missing,
bytecode_cache=missing,
):
"""Create a new overlay environment that shares all the data with the
current environment except for cache and the overridden attributes.
Extensions cannot be removed for an overlayed environment. An overlayed
environment automatically gets all the extensions of the environment it
is linked to plus optional extra extensions.
Creating overlays should happen after the initial environment was set
up completely. Not all attributes are truly linked, some are just
copied over so modifications on the original environment may not shine
through.
"""
args = dict(locals())
del args["self"], args["cache_size"], args["extensions"]
rv = object.__new__(self.__class__)
rv.__dict__.update(self.__dict__)
rv.overlayed = True
rv.linked_to = self
for key, value in args.items():
if value is not missing:
setattr(rv, key, value)
if cache_size is not missing:
rv.cache = create_cache(cache_size)
else:
rv.cache = copy_cache(self.cache)
rv.extensions = {}
for key, value in self.extensions.items():
rv.extensions[key] = value.bind(rv)
if extensions is not missing:
rv.extensions.update(load_extensions(rv, extensions))
return _environment_sanity_check(rv) | [
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|
cronyo/cronyo | cd5abab0871b68bf31b18aac934303928130a441 | cronyo/vendor/urllib3/util/retry.py | python | Retry.is_exhausted | (self) | return min(retry_counts) < 0 | Are we out of retries? | Are we out of retries? | [
"Are",
"we",
"out",
"of",
"retries?"
] | def is_exhausted(self):
""" Are we out of retries? """
retry_counts = (self.total, self.connect, self.read, self.redirect, self.status)
retry_counts = list(filter(None, retry_counts))
if not retry_counts:
return False
return min(retry_counts) < 0 | [
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|
Fenixin/Minecraft-Region-Fixer | bfafd378ceb65116e4ea48cab24f1e6394051978 | regionfixer_core/util.py | python | get_str_from_traceback | (ty, value, tb) | return s | Return a string from a traceback plus exception.
Inputs:
- ty -- Exception type
- value -- value of the traceback
- tb -- Traceback | Return a string from a traceback plus exception.
Inputs:
- ty -- Exception type
- value -- value of the traceback
- tb -- Traceback | [
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""" Return a string from a traceback plus exception.
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- tb -- Traceback
"""
t = traceback.format_exception(ty, value, tb)
s = str(ty) + "\n"
for i in t:
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return s | [
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|
zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_hxb2/lib/python3.5/site-packages/redis/client.py | python | int_or_none | (response) | return int(response) | [] | def int_or_none(response):
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|||
gdraheim/docker-systemctl-replacement | 9cbe1a00eb4bdac6ff05b96ca34ec9ed3d8fc06c | files/docker/systemctl.py | python | Systemctl.is_active_from | (self, conf) | return self.get_active_from(conf) == "active" | used in try-restart/other commands to check if needed. | used in try-restart/other commands to check if needed. | [
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orestis/pysmell | 14382f377f7759a1b6505120990898dd51f175e6 | pysmell/tm_dialog.py | python | item | (val) | [] | def item(val):
if isinstance(val, basestring):
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if isinstance(val, tuple):
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||||
Map-A-Droid/MAD | 81375b5c9ccc5ca3161eb487aa81469d40ded221 | mapadroid/patcher/patch_15.py | python | Patch._execute | (self) | [] | def _execute(self):
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settings = json.load(fh)
self.__convert_to_id(settings)
with open(self._application_args.mappings, 'w') as outfile:
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||||
fonttools/fonttools | 892322aaff6a89bea5927379ec06bc0da3dfb7df | Lib/fontTools/ttLib/tables/otConverters.py | python | AATLookupWithDataOffset.write | (self, writer, font, tableDict, value, repeatIndex=None) | [] | def write(self, writer, font, tableDict, value, repeatIndex=None):
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offsetByGlyph, offsetByData, dataLen = {}, {}, 0
compiledData = []
for glyph in sorted(value, key=font.getGlyphID):
subWriter = OTTableWriter()
value[glyph].compile(subWriter, font)
data = subWriter.getAllData()
offset = offsetByData.get(data, None)
if offset == None:
offset = dataLen
dataLen = dataLen + len(data)
offsetByData[data] = offset
compiledData.append(data)
offsetByGlyph[glyph] = offset
# For calculating the offsets to our AATLookup and data table,
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lookupWriter = writer.getSubWriter(offsetSize=4)
lookup = AATLookup('DataOffsets', None, None, UShort)
lookup.write(lookupWriter, font, tableDict, offsetByGlyph, None)
dataWriter = writer.getSubWriter(offsetSize=4)
writer.writeSubTable(lookupWriter)
writer.writeSubTable(dataWriter)
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||||
huggingface/transformers | 623b4f7c63f60cce917677ee704d6c93ee960b4b | src/transformers/utils/dummy_pt_objects.py | python | MPNetForMaskedLM.from_pretrained | (cls, *args, **kwargs) | [] | def from_pretrained(cls, *args, **kwargs):
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lohriialo/photoshop-scripting-python | 6b97da967a5d0a45e54f7c99631b29773b923f09 | api_reference/photoshop_2020.py | python | Document.MergeVisibleLayers | (self) | return self._oleobj_.InvokeTypes(1299608418, LCID, 1, (24, 0), (),) | flatten all visible layers in the document | flatten all visible layers in the document | [
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glutanimate/anki-addons-misc | 835ad28ec0927067b7acb4cd106e9b136205a838 | src/editor_field_history/editor_field_history.py | python | restoreEditorFields | (self, mode) | [] | def restoreEditorFields(self, mode):
if not self.note: # catch invalid state
return
# Gather note info
fld = self.currentField
if fld is None and mode in ("history", "field"):
# only necessary on anki20
tooltip("Please select a field whose last entry you want to restore.")
saveChanges(self, fld)
return False
did = self.parentWindow.deck_chooser.selectedId()
deck = self.mw.col.decks.nameOrNone(did)
model = self.note.model()
# Perform search
if deck:
query = 'deck:"%s"' % (deck)
results = self.note.col.findNotes(query)
if not results:
tooltip("Could not find any past notes in current deck.<br>"
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saveChanges(self, fld)
return False
results.sort(reverse=True)
# Get user selection
if mode == "history":
ret = historyRestore(self, mode, results, model, fld)
else:
ret = quickRestore(self, mode, results, model, fld)
if ret is False:
saveChanges(self, fld)
return False
# Save changes
saveChanges(self, fld) | [
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||||
lazylibrarian/LazyLibrarian | ae3c14e9db9328ce81765e094ab2a14ed7155624 | lib/requests/api.py | python | request | (method, url, **kwargs) | Constructs and sends a :class:`Request <Request>`.
:param method: method for the new :class:`Request` object.
:param url: URL for the new :class:`Request` object.
:param params: (optional) Dictionary or bytes to be sent in the query string for the :class:`Request`.
:param data: (optional) Dictionary, bytes, or file-like object to send in the body of the :class:`Request`.
:param json: (optional) json data to send in the body of the :class:`Request`.
:param headers: (optional) Dictionary of HTTP Headers to send with the :class:`Request`.
:param cookies: (optional) Dict or CookieJar object to send with the :class:`Request`.
:param files: (optional) Dictionary of ``'name': file-like-objects`` (or ``{'name': file-tuple}``) for multipart encoding upload.
``file-tuple`` can be a 2-tuple ``('filename', fileobj)``, 3-tuple ``('filename', fileobj, 'content_type')``
or a 4-tuple ``('filename', fileobj, 'content_type', custom_headers)``, where ``'content-type'`` is a string
defining the content type of the given file and ``custom_headers`` a dict-like object containing additional headers
to add for the file.
:param auth: (optional) Auth tuple to enable Basic/Digest/Custom HTTP Auth.
:param timeout: (optional) How long to wait for the server to send data
before giving up, as a float, or a :ref:`(connect timeout, read
timeout) <timeouts>` tuple.
:type timeout: float or tuple
:param allow_redirects: (optional) Boolean. Enable/disable GET/OPTIONS/POST/PUT/PATCH/DELETE/HEAD redirection.
:type allow_redirects: bool
:param proxies: (optional) Dictionary mapping protocol to the URL of the proxy.
:param verify: (optional) whether the SSL cert will be verified. A CA_BUNDLE path can also be provided. Defaults to ``True``.
:param stream: (optional) if ``False``, the response content will be immediately downloaded.
:param cert: (optional) if String, path to ssl client cert file (.pem). If Tuple, ('cert', 'key') pair.
:return: :class:`Response <Response>` object
:rtype: requests.Response
Usage::
>>> import requests
>>> req = requests.request('GET', 'http://httpbin.org/get')
<Response [200]> | Constructs and sends a :class:`Request <Request>`. | [
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:param method: method for the new :class:`Request` object.
:param url: URL for the new :class:`Request` object.
:param params: (optional) Dictionary or bytes to be sent in the query string for the :class:`Request`.
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:param cookies: (optional) Dict or CookieJar object to send with the :class:`Request`.
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:return: :class:`Response <Response>` object
:rtype: requests.Response
Usage::
>>> import requests
>>> req = requests.request('GET', 'http://httpbin.org/get')
<Response [200]>
"""
# By using the 'with' statement we are sure the session is closed, thus we
# avoid leaving sockets open which can trigger a ResourceWarning in some
# cases, and look like a memory leak in others.
with sessions.Session() as session:
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AutodeskRoboticsLab/Mimic | 85447f0d346be66988303a6a054473d92f1ed6f4 | mFIZ/scripts/mFIZ_extern/serial/serialutil.py | python | SerialBase.__init__ | (self,
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bytesize=EIGHTBITS,
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stopbits=STOPBITS_ONE,
timeout=None,
xonxoff=False,
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Initialize comm port object. If a "port" is given, then the port will be
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timeout=None,
xonxoff=False,
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self._timeout = None
self._write_timeout = None
self._xonxoff = None
self._rtscts = None
self._dsrdtr = None
self._inter_byte_timeout = None
self._rs485_mode = None # disabled by default
self._rts_state = True
self._dtr_state = True
self._break_state = False
# assign values using get/set methods using the properties feature
self.port = port
self.baudrate = baudrate
self.bytesize = bytesize
self.parity = parity
self.stopbits = stopbits
self.timeout = timeout
self.write_timeout = write_timeout
self.xonxoff = xonxoff
self.rtscts = rtscts
self.dsrdtr = dsrdtr
self.inter_byte_timeout = inter_byte_timeout
# watch for backward compatible kwargs
if 'writeTimeout' in kwargs:
self.write_timeout = kwargs.pop('writeTimeout')
if 'interCharTimeout' in kwargs:
self.inter_byte_timeout = kwargs.pop('interCharTimeout')
if kwargs:
raise ValueError('unexpected keyword arguments: {!r}'.format(kwargs))
if port is not None:
self.open() | [
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||
ajinabraham/OWASP-Xenotix-XSS-Exploit-Framework | cb692f527e4e819b6c228187c5702d990a180043 | bin/x86/Debug/scripting_engine/Lib/site-packages/lxml/html/diff.py | python | split_words | (text) | return words | Splits some text into words. Includes trailing whitespace (one
space) on each word when appropriate. | Splits some text into words. Includes trailing whitespace (one
space) on each word when appropriate. | [
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] | def split_words(text):
""" Splits some text into words. Includes trailing whitespace (one
space) on each word when appropriate. """
if not text or not text.strip():
return []
words = [w + ' ' for w in text.strip().split()]
if not end_whitespace_re.search(text):
words[-1] = words[-1][:-1]
return words | [
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|
google/jax | bebe9845a873b3203f8050395255f173ba3bbb71 | jax/experimental/sparse/transform.py | python | sparsify | (f, use_tracer=False) | Experimental sparsification transform.
Examples:
Decorate JAX functions to make them compatible with :class:`jax.experimental.sparse.BCOO`
matrices:
>>> from jax.experimental import sparse
>>> @sparse.sparsify
... def f(M, v):
... return 2 * M.T @ v
>>> M = sparse.BCOO.fromdense(jnp.arange(12).reshape(3, 4))
>>> v = jnp.array([3, 4, 2])
>>> f(M, v)
DeviceArray([ 64, 82, 100, 118], dtype=int32) | Experimental sparsification transform. | [
"Experimental",
"sparsification",
"transform",
"."
] | def sparsify(f, use_tracer=False):
"""Experimental sparsification transform.
Examples:
Decorate JAX functions to make them compatible with :class:`jax.experimental.sparse.BCOO`
matrices:
>>> from jax.experimental import sparse
>>> @sparse.sparsify
... def f(M, v):
... return 2 * M.T @ v
>>> M = sparse.BCOO.fromdense(jnp.arange(12).reshape(3, 4))
>>> v = jnp.array([3, 4, 2])
>>> f(M, v)
DeviceArray([ 64, 82, 100, 118], dtype=int32)
"""
if use_tracer:
return _sparsify_with_tracer(f)
else:
return _sparsify_with_interpreter(f) | [
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||
pyscf/pyscf | 0adfb464333f5ceee07b664f291d4084801bae64 | pyscf/geomopt/geometric_solver.py | python | optimize | (method, assert_convergence=ASSERT_CONV,
include_ghost=INCLUDE_GHOST, constraints=None, callback=None,
maxsteps=100, **kwargs) | return kernel(method, assert_convergence=assert_convergence, include_ghost=include_ghost,
constraints=constraints, callback=callback, maxsteps=maxsteps, **kwargs)[1] | Optimize geometry with geomeTRIC library for the given method.
To adjust the convergence threshold, parameters can be set in kwargs as
below:
.. code-block:: python
conv_params = { # They are default settings
'convergence_energy': 1e-6, # Eh
'convergence_grms': 3e-4, # Eh/Bohr
'convergence_gmax': 4.5e-4, # Eh/Bohr
'convergence_drms': 1.2e-3, # Angstrom
'convergence_dmax': 1.8e-3, # Angstrom
}
from pyscf import geometric_solver
newmol = geometric_solver.optimize(method, **conv_params) | Optimize geometry with geomeTRIC library for the given method. | [
"Optimize",
"geometry",
"with",
"geomeTRIC",
"library",
"for",
"the",
"given",
"method",
"."
] | def optimize(method, assert_convergence=ASSERT_CONV,
include_ghost=INCLUDE_GHOST, constraints=None, callback=None,
maxsteps=100, **kwargs):
'''Optimize geometry with geomeTRIC library for the given method.
To adjust the convergence threshold, parameters can be set in kwargs as
below:
.. code-block:: python
conv_params = { # They are default settings
'convergence_energy': 1e-6, # Eh
'convergence_grms': 3e-4, # Eh/Bohr
'convergence_gmax': 4.5e-4, # Eh/Bohr
'convergence_drms': 1.2e-3, # Angstrom
'convergence_dmax': 1.8e-3, # Angstrom
}
from pyscf import geometric_solver
newmol = geometric_solver.optimize(method, **conv_params)
'''
# MRH, 07/23/2019: name all explicit kwargs for forward compatibility
return kernel(method, assert_convergence=assert_convergence, include_ghost=include_ghost,
constraints=constraints, callback=callback, maxsteps=maxsteps, **kwargs)[1] | [
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|
i-pan/kaggle-rsna18 | 2db498fe99615d935aa676f04847d0c562fd8e46 | models/RetinaNet/keras_retinanet/models/vgg.py | python | VGGBackbone.retinanet | (self, *args, **kwargs) | return vgg_retinanet(*args, backbone=self.backbone, **kwargs) | Returns a retinanet model using the correct backbone. | Returns a retinanet model using the correct backbone. | [
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] | def retinanet(self, *args, **kwargs):
""" Returns a retinanet model using the correct backbone.
"""
return vgg_retinanet(*args, backbone=self.backbone, **kwargs) | [
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|
aaPanel/BaoTa | 9bb1336f31ae2893ab513af7a3efed633338c64b | class/clusterDns.py | python | aliyun.check_result | (self,req) | return result | @name 检查响应结果
@author hwliang<2020-10-30>
@param req<requests> 响应结果
@return mixed | [] | def check_result(self,req):
'''
@name 检查响应结果
@author hwliang<2020-10-30>
@param req<requests> 响应结果
@return mixed
'''
result = req.json()
if req.status_code != 200:
if result['Code'] == 'IncorrectDomainUser' or result['Code'] == 'InvalidDomainName.NoExist':
return public.returnMsg(False,"这个阿里云账户下面不存在这个域名")
elif result['Code'] == 'InvalidAccessKeyId.NotFound' or result['Code'] == 'SignatureDoesNotMatch':
return public.returnMsg(False,"API密钥错误")
else:
return public.returnMsg(False,result['Message'])
return result | [
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||
scrapy/scrapy | b04cfa48328d5d5749dca6f50fa34e0cfc664c89 | scrapy/extensions/memusage.py | python | MemoryUsage.engine_started | (self) | [] | def engine_started(self):
self.crawler.stats.set_value('memusage/startup', self.get_virtual_size())
self.tasks = []
tsk = task.LoopingCall(self.update)
self.tasks.append(tsk)
tsk.start(self.check_interval, now=True)
if self.limit:
tsk = task.LoopingCall(self._check_limit)
self.tasks.append(tsk)
tsk.start(self.check_interval, now=True)
if self.warning:
tsk = task.LoopingCall(self._check_warning)
self.tasks.append(tsk)
tsk.start(self.check_interval, now=True) | [
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||||
IdentityPython/pysaml2 | 6badb32d212257bd83ffcc816f9b625f68281b47 | src/saml2/ws/wstrust.py | python | participants_type__primary_from_string | (xml_string) | return saml2.create_class_from_xml_string(ParticipantsType_Primary, xml_string) | [] | def participants_type__primary_from_string(xml_string):
return saml2.create_class_from_xml_string(ParticipantsType_Primary, xml_string) | [
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|||
marcosfede/algorithms | 1ee7c815f9d556c9cef4d4b0d21ee3a409d21629 | tree/binary_tree/is_symmetric/is_symmetric.py | python | _is_symmetric | (p, q) | return _is_symmetric(p.left, q.right) and _is_symmetric(p.right, q.left) | [] | def _is_symmetric(p, q):
if p is None and q is None:
return True
if p is None or q is None or q.val != p.val:
return False
return _is_symmetric(p.left, q.right) and _is_symmetric(p.right, q.left) | [
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|||
deeptools/deepTools | ac42d29c298c026aa0c53c9db2553087ebc86b97 | deeptools/plotFingerprint.py | python | getCHANCE | (args, idx, mat) | return [pcenrich, diffenrich, CHANCEdivergence] | Compute the CHANCE p-value
1) In short, sort IP from lowest to highest, cosorting input at the same time.
2) Choose the argmax of the difference of the cumsum() of the above
3) Determine a scale factor according to the ratio at the position at step 2. | Compute the CHANCE p-value | [
"Compute",
"the",
"CHANCE",
"p",
"-",
"value"
] | def getCHANCE(args, idx, mat):
"""
Compute the CHANCE p-value
1) In short, sort IP from lowest to highest, cosorting input at the same time.
2) Choose the argmax of the difference of the cumsum() of the above
3) Determine a scale factor according to the ratio at the position at step 2.
"""
# Get the index of the reference sample
if args.JSDsample not in args.bamfiles:
return [np.NAN, np.NAN, np.NAN]
refIdx = args.bamfiles.index(args.JSDsample)
if refIdx == idx:
return [np.NAN, np.NAN, np.NAN]
subMatrix = np.copy(mat[:, [idx, refIdx]])
subMatrix[np.isnan(subMatrix)] = 0
subMatrix = subMatrix[subMatrix[:, 0].argsort(), :]
# Find the CHANCE statistic, which is the point of maximus difference
cs = np.cumsum(subMatrix, axis=0)
normed = cs / np.max(cs, axis=0).astype(float)
csdiff = normed[:, 1] - normed[:, 0]
k = np.argmax(csdiff)
if csdiff[k] < 1e-6:
# Don't bother with negative values
return [0, 0, 0]
p = normed[k, 0] # Percent enrichment in IP
q = normed[k, 1] # Percent enrichment in input
pcenrich = 100 * (len(csdiff) - k) / float(len(csdiff))
diffenrich = 100.0 * (q - p)
# CHANCE's JS divergence with binary entropy
# Its p value is a ztest of this, which is largely useless IMO
M = (p + q) / 2.0
CHANCEdivergence = 0.5 * (binRelEntropy(p, M) + binRelEntropy(q, M))
CHANCEdivergence = np.sqrt(CHANCEdivergence)
return [pcenrich, diffenrich, CHANCEdivergence] | [
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|
FSecureLABS/Jandroid | e31d0dab58a2bfd6ed8e0a387172b8bd7c893436 | libs/platform-tools/platform-tools_linux/systrace/catapult/devil/devil/android/device_utils.py | python | DeviceUtils._ClearCache | (self) | Clears all caches. | Clears all caches. | [
"Clears",
"all",
"caches",
"."
] | def _ClearCache(self):
"""Clears all caches."""
for client in self._client_caches:
self._client_caches[client].clear()
self._cache = {
# Map of packageId -> list of on-device .apk paths
'package_apk_paths': {},
# Set of packageId that were loaded from LoadCacheData and not yet
# verified.
'package_apk_paths_to_verify': set(),
# Map of packageId -> set of on-device .apk checksums
'package_apk_checksums': {},
# Map of property_name -> value
'getprop': {},
# Map of device_path -> [ignore_other_files, map of path->checksum]
'device_path_checksums': {},
# Location of sdcard ($EXTERNAL_STORAGE).
'external_storage': None,
# Token used to detect when LoadCacheData is stale.
'token': None,
'prev_token': None,
} | [
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||
microsoft/botbuilder-python | 3d410365461dc434df59bdfeaa2f16d28d9df868 | libraries/botbuilder-core/botbuilder/core/message_factory.py | python | MessageFactory.carousel | (
attachments: List[Attachment],
text: str = None,
speak: str = None,
input_hint: Union[InputHints, str] = None,
) | return attachment_activity(
AttachmentLayoutTypes.carousel, attachments, text, speak, input_hint
) | Returns a message that will display a set of attachments using a carousel layout.
:Example:
message = MessageFactory.carousel([CardFactory.hero_card(HeroCard(title='title1',
images=[CardImage(url='imageUrl1')],
buttons=[CardAction(title='button1')])),
CardFactory.hero_card(HeroCard(title='title2',
images=[CardImage(url='imageUrl2')],
buttons=[CardAction(title='button2')])),
CardFactory.hero_card(HeroCard(title='title3',
images=[CardImage(url='imageUrl3')],
buttons=[CardAction(title='button3')]))])
await context.send_activity(message)
:param attachments:
:param text:
:param speak:
:param input_hint:
:return: | Returns a message that will display a set of attachments using a carousel layout. | [
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"that",
"will",
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"layout",
"."
] | def carousel(
attachments: List[Attachment],
text: str = None,
speak: str = None,
input_hint: Union[InputHints, str] = None,
) -> Activity:
"""
Returns a message that will display a set of attachments using a carousel layout.
:Example:
message = MessageFactory.carousel([CardFactory.hero_card(HeroCard(title='title1',
images=[CardImage(url='imageUrl1')],
buttons=[CardAction(title='button1')])),
CardFactory.hero_card(HeroCard(title='title2',
images=[CardImage(url='imageUrl2')],
buttons=[CardAction(title='button2')])),
CardFactory.hero_card(HeroCard(title='title3',
images=[CardImage(url='imageUrl3')],
buttons=[CardAction(title='button3')]))])
await context.send_activity(message)
:param attachments:
:param text:
:param speak:
:param input_hint:
:return:
"""
return attachment_activity(
AttachmentLayoutTypes.carousel, attachments, text, speak, input_hint
) | [
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|
frescobaldi/frescobaldi | 301cc977fc4ba7caa3df9e4bf905212ad5d06912 | frescobaldi_app/file_import/__init__.py | python | FileImport.import_musicxml | (self) | Opens a MusicXML file. Converts it to ly by using musicxml2ly. | Opens a MusicXML file. Converts it to ly by using musicxml2ly. | [
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"a",
"MusicXML",
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] | def import_musicxml(self):
"""Opens a MusicXML file. Converts it to ly by using musicxml2ly."""
filetypes = '{0} (*.xml *.musicxml *.mxl);;{1} (*)'.format(
_("MusicXML Files"), _("All Files"))
caption = _("dialog title", "Import a MusicXML file")
self.do_import(filetypes, caption) | [
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||
apache/libcloud | 90971e17bfd7b6bb97b2489986472c531cc8e140 | libcloud/compute/drivers/vsphere.py | python | VSphereNodeDriver.find_by_uuid | (self, node_uuid) | return vm | Searches VMs for a given uuid
returns pyVmomi.VmomiSupport.vim.VirtualMachine | Searches VMs for a given uuid
returns pyVmomi.VmomiSupport.vim.VirtualMachine | [
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] | def find_by_uuid(self, node_uuid):
"""Searches VMs for a given uuid
returns pyVmomi.VmomiSupport.vim.VirtualMachine
"""
vm = self.connection.content.searchIndex.FindByUuid(None, node_uuid, True, True)
if not vm:
# perhaps it is a moid
vm = self._get_item_by_moid("VirtualMachine", node_uuid)
if not vm:
raise LibcloudError("Unable to locate VirtualMachine.", driver=self)
return vm | [
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|
rosedu/wouso | 82fd175354432d29b6623c150d5d82c4fe5260de | wouso/interface/activity/models.py | python | Activity.get_player_activity | (cls, player, **kwargs) | return cls.filter_activity(query) | Return an user's activity. | Return an user's activity. | [
"Return",
"an",
"user",
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"."
] | def get_player_activity(cls, player, **kwargs):
"""
Return an user's activity.
"""
query = cls.queryset().filter(Q(user_to=player) | Q(user_from=player)).order_by('-timestamp')
return cls.filter_activity(query) | [
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|
aws-samples/ecs-blue-green-deployment | f319ca8a1e5c90ad48beaa67c4f6ea6fa51f2efb | scripts/deployer.py | python | find_beta_image_identifier | (targetgrouparn) | return identifier,imagesha | Queries the tags on TargetGroups
Args:
targetgrouparn - Amazon ARN of the Target group that needs to be queried for the Tags
Returns:
identifier : tag key value of the target group , with KeyName as "Identifier"
sha: Sha or the image id running on target group
Raises:
Exception: Any exception thrown by handler | Queries the tags on TargetGroups | [
"Queries",
"the",
"tags",
"on",
"TargetGroups"
] | def find_beta_image_identifier(targetgrouparn):
"""Queries the tags on TargetGroups
Args:
targetgrouparn - Amazon ARN of the Target group that needs to be queried for the Tags
Returns:
identifier : tag key value of the target group , with KeyName as "Identifier"
sha: Sha or the image id running on target group
Raises:
Exception: Any exception thrown by handler
"""
response = elb_client.describe_tags(ResourceArns=[targetgrouparn])
identifier = None
imagesha = None
for tags in response['TagDescriptions']:
for tag in tags['Tags']:
if tag['Key'] == "Identifier":
print("Image identifier string on " + targetgrouparn + " : " + tag['Value'])
identifier = tag['Value']
if tag['Key'] == "Image":
imagesha = tag['Value']
return identifier,imagesha | [
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|
nodesign/weio | 1d67d705a5c36a2e825ad13feab910b0aca9a2e8 | things/input/environmental/digitemp/device/termometer.py | python | OneWireTemperatureSensor._reset | (self) | Send reset pulse, wait for presence and then select the device. | Send reset pulse, wait for presence and then select the device. | [
"Send",
"reset",
"pulse",
"wait",
"for",
"presence",
"and",
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] | def _reset(self):
"""
Send reset pulse, wait for presence and then select the device.
"""
if self.single_mode:
self._skip_ROM() # because it is single device
else:
self._match_ROM(self.rom_code) | [
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||
w3h/isf | 6faf0a3df185465ec17369c90ccc16e2a03a1870 | lib/thirdparty/scapy/contrib/gsm_um.py | python | activateAaPdpContextReject | (ProtocolConfigurationOptions_presence=0) | return packet | ACTIVATE AA PDP CONTEXT REJECT Section 9.5.12 | ACTIVATE AA PDP CONTEXT REJECT Section 9.5.12 | [
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"PDP",
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"Section",
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"5",
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] | def activateAaPdpContextReject(ProtocolConfigurationOptions_presence=0):
"""ACTIVATE AA PDP CONTEXT REJECT Section 9.5.12"""
a = TpPd(pd=0x8)
b = MessageType(mesType=0x52) # 01010010
c = SmCause()
packet = a / b / c
if ProtocolConfigurationOptions_presence is 1:
d = ProtocolConfigurationOptions(ieiPCO=0x27)
packet = packet / d
return packet | [
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|
openimages/dataset | 077282972acd0ad8628f1526760ad239a38a8a97 | tools/compute_bottleneck.py | python | main | (args) | [] | def main(args):
if not os.path.exists(FLAGS.checkpoint):
tf.logging.fatal(
'Checkpoint %s does not exist. Have you download it? See tools/download_data.sh',
FLAGS.checkpoint)
g = tf.Graph()
with g.as_default():
input_image = PreprocessImage(FLAGS.image_path[0])
with slim.arg_scope(inception.inception_v3_arg_scope()):
logits, end_points = inception.inception_v3(
input_image, num_classes=FLAGS.num_classes, is_training=False)
bottleneck = end_points['PreLogits']
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer(),
tf.tables_initializer())
saver = tf_saver.Saver()
sess = tf.Session()
saver.restore(sess, FLAGS.checkpoint)
# Run the evaluation on the image
bottleneck_eval = np.squeeze(sess.run(bottleneck))
first = True
for val in bottleneck_eval:
if not first:
sys.stdout.write(",")
first = False
sys.stdout.write('{:.3f}'.format(val))
sys.stdout.write('\n') | [
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||||
lovelylain/pyctp | fd304de4b50c4ddc31a4190b1caaeb5dec66bc5d | lts/ctp/__init__.py | python | TraderApi.ReqQryTradingAccount | (self, pQryTradingAccount, nRequestID) | return 0 | 请求查询资金账户 | 请求查询资金账户 | [
"请求查询资金账户"
] | def ReqQryTradingAccount(self, pQryTradingAccount, nRequestID):
"""请求查询资金账户"""
return 0 | [
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|
ask/carrot | 5889a25cd2e274642071c9bba39772f4b3e3d9da | carrot/connection.py | python | BrokerConnection.info | (self) | return {"hostname": self.hostname,
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backend_cls = self.backend_cls or "amqplib"
port = self.port or self.create_backend().default_port
return {"hostname": self.hostname,
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|
gramps-project/gramps | 04d4651a43eb210192f40a9f8c2bad8ee8fa3753 | gramps/gui/views/treemodels/treebasemodel.py | python | TreeBaseModel.__rebuild_filter | (self, dfilter, skip, items, gen_cursor, data_map,
add_func) | Rebuild the data map for a single Gramps object type, where a filter
is applied. | Rebuild the data map for a single Gramps object type, where a filter
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"""
Rebuild the data map for a single Gramps object type, where a filter
is applied.
"""
pmon = progressdlg.ProgressMonitor(
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title=_("Loading items..."))
status_ppl = progressdlg.LongOpStatus(total_steps=items,
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pmon.add_op(status_ppl)
self.__total += items
assert not skip
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cdb = CacheProxyDb(self.db)
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status_ppl.heartbeat()
add_func(handle, data)
self.__displayed += 1
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||
securesystemslab/zippy | ff0e84ac99442c2c55fe1d285332cfd4e185e089 | zippy/benchmarks/src/benchmarks/sympy/sympy/galgebra/ncutil.py | python | get_commutative_coef | (expr) | return S.One | [] | def get_commutative_coef(expr):
if isinstance(expr, Mul):
(coefs, bases) = expr.args_cnc()
return Mul(*coefs)
return S.One | [
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|||
IJDykeman/wangTiles | 7c1ee2095ebdf7f72bce07d94c6484915d5cae8b | experimental_code/tiles_3d/venv/lib/python2.7/site-packages/pip/_vendor/distlib/locators.py | python | Page.__init__ | (self, data, url) | Initialise an instance with the Unicode page contents and the URL they
came from. | Initialise an instance with the Unicode page contents and the URL they
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"""
Initialise an instance with the Unicode page contents and the URL they
came from.
"""
self.data = data
self.base_url = self.url = url
m = self._base.search(self.data)
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||
GNS3/gns3-gui | da8adbaa18ab60e053af2a619efd468f4c8950f3 | gns3/base_node.py | python | BaseNode.reset | (cls) | Reset the instance count. | Reset the instance count. | [
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Reset the instance count.
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||
python-trio/trio | 4edfd41bd5519a2e626e87f6c6ca9fb32b90a6f4 | trio/_core/_traps.py | python | cancel_shielded_checkpoint | () | return (await _async_yield(CancelShieldedCheckpoint)).unwrap() | Introduce a schedule point, but not a cancel point.
This is *not* a :ref:`checkpoint <checkpoints>`, but it is half of a
checkpoint, and when combined with :func:`checkpoint_if_cancelled` it can
make a full checkpoint.
Equivalent to (but potentially more efficient than)::
with trio.CancelScope(shield=True):
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This is *not* a :ref:`checkpoint <checkpoints>`, but it is half of a
checkpoint, and when combined with :func:`checkpoint_if_cancelled` it can
make a full checkpoint.
Equivalent to (but potentially more efficient than)::
with trio.CancelScope(shield=True):
await trio.lowlevel.checkpoint()
"""
return (await _async_yield(CancelShieldedCheckpoint)).unwrap() | [
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|
thaines/helit | 04bd36ee0fb6b762c63d746e2cd8813641dceda9 | video/video_node.py | python | VideoNode.frameCount | (self) | Returns the number of times you can call nextFrame before it starts returning None. | Returns the number of times you can call nextFrame before it starts returning None. | [
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||
mrlesmithjr/Ansible | d44f0dc0d942bdf3bf7334b307e6048f0ee16e36 | roles/ansible-vsphere-management/scripts/pdns/lib/python2.7/site-packages/pip/index.py | python | HTMLPage.links | (self) | Yields all links in the page | Yields all links in the page | [
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] | def links(self):
"""Yields all links in the page"""
for anchor in self.parsed.findall(".//a"):
if anchor.get("href"):
href = anchor.get("href")
url = self.clean_link(
urllib_parse.urljoin(self.base_url, href)
)
pyrequire = anchor.get('data-requires-python')
pyrequire = unescape(pyrequire) if pyrequire else None
yield Link(url, self, requires_python=pyrequire) | [
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||
karanchahal/distiller | a17ec06cbeafcdd2aea19d7c7663033c951392f5 | models/cifar10/shufflenet.py | python | ShuffleNetG3 | () | return ShuffleNet(cfg) | [] | def ShuffleNetG3():
cfg = {
'out_planes': [240,480,960],
'num_blocks': [4,8,4],
'groups': 3
}
return ShuffleNet(cfg) | [
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|||
Azure/azure-devops-cli-extension | 11334cd55806bef0b99c3bee5a438eed71e44037 | azure-devops/azext_devops/devops_sdk/released/build/build_client.py | python | BuildClient.add_build_tags | (self, tags, project, build_id) | return self._deserialize('[str]', self._unwrap_collection(response)) | AddBuildTags.
Adds tags to a build.
:param [str] tags: The tags to add.
:param str project: Project ID or project name
:param int build_id: The ID of the build.
:rtype: [str] | AddBuildTags.
Adds tags to a build.
:param [str] tags: The tags to add.
:param str project: Project ID or project name
:param int build_id: The ID of the build.
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:param int build_id: The ID of the build.
:rtype: [str]
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route_values = {}
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|
open-io/oio-sds | 16041950b6056a55d5ce7ca77795defe6dfa6c61 | oio/directory/client.py | python | DirectoryClient.unlink | (self, account=None, reference=None, service_type=None, cid=None,
**kwargs) | Remove an associated service from the reference | Remove an associated service from the reference | [
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] | def unlink(self, account=None, reference=None, service_type=None, cid=None,
**kwargs):
"""
Remove an associated service from the reference
"""
params = self._make_params(account, reference, service_type, cid=cid)
_resp, _body = self._request('POST', '/unlink', params=params,
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||
exaile/exaile | a7b58996c5c15b3aa7b9975ac13ee8f784ef4689 | xlgui/preferences/widgets.py | python | CheckConditional.on_check_condition | (self) | return self.get_condition_value() | Specifies the condition to meet
:returns: Whether the condition is met or not
:rtype: bool | Specifies the condition to meet | [
"Specifies",
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"condition",
"to",
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] | def on_check_condition(self):
"""
Specifies the condition to meet
:returns: Whether the condition is met or not
:rtype: bool
"""
return self.get_condition_value() | [
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|
googleads/google-ads-python | 2a1d6062221f6aad1992a6bcca0e7e4a93d2db86 | google/ads/googleads/v9/services/services/asset_field_type_view_service/client.py | python | AssetFieldTypeViewServiceClient.from_service_account_file | (cls, filename: str, *args, **kwargs) | return cls(*args, **kwargs) | Creates an instance of this client using the provided credentials
file.
Args:
filename (str): The path to the service account private key json
file.
args: Additional arguments to pass to the constructor.
kwargs: Additional arguments to pass to the constructor.
Returns:
AssetFieldTypeViewServiceClient: The constructed client. | Creates an instance of this client using the provided credentials
file. | [
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"""Creates an instance of this client using the provided credentials
file.
Args:
filename (str): The path to the service account private key json
file.
args: Additional arguments to pass to the constructor.
kwargs: Additional arguments to pass to the constructor.
Returns:
AssetFieldTypeViewServiceClient: The constructed client.
"""
credentials = service_account.Credentials.from_service_account_file(
filename
)
kwargs["credentials"] = credentials
return cls(*args, **kwargs) | [
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|
okpy/ok | 50a00190f05363d096478dd8e53aa1a36dd40c4a | migrations/env.py | python | run_migrations_offline | () | Run migrations in 'offline' mode.
This configures the context with just a URL
and not an Engine, though an Engine is acceptable
here as well. By skipping the Engine creation
we don't even need a DBAPI to be available.
Calls to context.execute() here emit the given string to the
script output. | Run migrations in 'offline' mode. | [
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] | def run_migrations_offline():
"""Run migrations in 'offline' mode.
This configures the context with just a URL
and not an Engine, though an Engine is acceptable
here as well. By skipping the Engine creation
we don't even need a DBAPI to be available.
Calls to context.execute() here emit the given string to the
script output.
"""
url = config.get_main_option("sqlalchemy.url")
context.configure(url=url)
with context.begin_transaction():
context.run_migrations() | [
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||
Delta-ML/delta | 31dfebc8f20b7cb282b62f291ff25a87e403cc86 | delta/data/feat/speech_feature.py | python | extract_fbank | (samples,
sr=8000,
winlen=0.025,
winstep=0.01,
feature_size=40) | return np.stack([feat, delta, _delta_delta], axis=-1) | extract logfbank with delta and delta-delta
Return:
ndarray of shape [nfrmae, feature_size * 3] | extract logfbank with delta and delta-delta
Return:
ndarray of shape [nfrmae, feature_size * 3] | [
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sr=8000,
winlen=0.025,
winstep=0.01,
feature_size=40):
''' extract logfbank with delta and delta-delta
Return:
ndarray of shape [nfrmae, feature_size * 3]
'''
feat = psf.logfbank(
samples,
nfilt=feature_size,
samplerate=sr,
winlen=winlen,
winstep=winstep,
lowfreq=0,
highfreq=None,
preemph=0.97)
delta = psf.delta(feat, N=2)
_delta_delta = psf.delta(delta, N=2)
return np.stack([feat, delta, _delta_delta], axis=-1) | [
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|
john-hewitt/structural-probes | 4c2e265d6bd071e6ab380fd9806e4c6a128b5e97 | structural-probes/probe.py | python | OneWordPSDProbe.forward | (self, batch) | return norms | Computes all n depths after projection
for each sentence in a batch.
Computes (Bh_i)^T(Bh_i) for all i
Args:
batch: a batch of word representations of the shape
(batch_size, max_seq_len, representation_dim)
Returns:
A tensor of depths of shape (batch_size, max_seq_len) | Computes all n depths after projection
for each sentence in a batch. | [
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] | def forward(self, batch):
""" Computes all n depths after projection
for each sentence in a batch.
Computes (Bh_i)^T(Bh_i) for all i
Args:
batch: a batch of word representations of the shape
(batch_size, max_seq_len, representation_dim)
Returns:
A tensor of depths of shape (batch_size, max_seq_len)
"""
transformed = torch.matmul(batch, self.proj)
batchlen, seqlen, rank = transformed.size()
norms = torch.bmm(transformed.view(batchlen* seqlen, 1, rank),
transformed.view(batchlen* seqlen, rank, 1))
norms = norms.view(batchlen, seqlen)
return norms | [
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|
chribsen/simple-machine-learning-examples | dc94e52a4cebdc8bb959ff88b81ff8cfeca25022 | venv/lib/python2.7/site-packages/pandas/stats/ols.py | python | OLS.rmse | (self) | return self._rmse_raw | Returns the rmse value. | Returns the rmse value. | [
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"""Returns the rmse value."""
return self._rmse_raw | [
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|
kylebebak/Requester | 4a9f9f051fa5fc951a8f7ad098a328261ca2db97 | deps/oauthlib/oauth2/rfc6749/utils.py | python | list_to_scope | (scope) | Convert a list of scopes to a space separated string. | Convert a list of scopes to a space separated string. | [
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] | def list_to_scope(scope):
"""Convert a list of scopes to a space separated string."""
if isinstance(scope, unicode_type) or scope is None:
return scope
elif isinstance(scope, (set, tuple, list)):
return " ".join([unicode_type(s) for s in scope])
else:
raise ValueError("Invalid scope (%s), must be string, tuple, set, or list." % scope) | [
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||
aliyun/aliyun-openapi-python-sdk | bda53176cc9cf07605b1cf769f0df444cca626a0 | aliyun-python-sdk-core-v3/aliyunsdkcore/vendored/requests/utils.py | python | guess_filename | (obj) | Tries to guess the filename of the given object. | Tries to guess the filename of the given object. | [
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] | def guess_filename(obj):
"""Tries to guess the filename of the given object."""
name = getattr(obj, 'name', None)
if (name and isinstance(name, basestring) and name[0] != '<' and
name[-1] != '>'):
return os.path.basename(name) | [
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||
zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/lib/python2.7/site-packages/setuptools/extern/__init__.py | python | VendorImporter.search_path | (self) | Search first the vendor package then as a natural package. | Search first the vendor package then as a natural package. | [
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] | def search_path(self):
"""
Search first the vendor package then as a natural package.
"""
yield self.vendor_pkg + '.'
yield '' | [
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||
mne-tools/mne-python | f90b303ce66a8415e64edd4605b09ac0179c1ebf | mne/viz/topo.py | python | _erfimage_imshow_unified | (bn, ch_idx, tmin, tmax, vmin, vmax, ylim=None,
data=None, epochs=None, sigma=None, order=None,
scalings=None, vline=None, x_label=None,
y_label=None, colorbar=False, cmap='RdBu_r',
vlim_array=None) | Plot erfimage topography using a single axis. | Plot erfimage topography using a single axis. | [
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] | def _erfimage_imshow_unified(bn, ch_idx, tmin, tmax, vmin, vmax, ylim=None,
data=None, epochs=None, sigma=None, order=None,
scalings=None, vline=None, x_label=None,
y_label=None, colorbar=False, cmap='RdBu_r',
vlim_array=None):
"""Plot erfimage topography using a single axis."""
from scipy import ndimage
_compute_ax_scalings(bn, (tmin, tmax), (0, len(epochs.events)))
ax = bn.ax
data_lines = bn.data_lines
extent = (bn.x_t + bn.x_s * tmin, bn.x_t + bn.x_s * tmax, bn.y_t,
bn.y_t + bn.y_s * len(epochs.events))
this_data = data[:, ch_idx, :]
vmin, vmax = (None, None) if vlim_array is None else vlim_array[ch_idx]
if callable(order):
order = order(epochs.times, this_data)
if order is not None:
this_data = this_data[order]
if sigma > 0.:
this_data = ndimage.gaussian_filter1d(this_data, sigma=sigma, axis=0)
data_lines.append(ax.imshow(this_data, extent=extent, aspect='auto',
origin='lower', vmin=vmin, vmax=vmax,
picker=True, cmap=cmap,
interpolation='nearest')) | [
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||
jaychsu/algorithm | 87dac5456b74a515dd97507ac68e9b8588066a04 | other/uneven_random_get.py | python | uneven_random_get | (options, rate) | return options[0] | unevenly fetch the option according to the corresponding rate
:type options: list[str]
:type rate: list[num]
:rtype: str | unevenly fetch the option according to the corresponding rate
:type options: list[str]
:type rate: list[num]
:rtype: str | [
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"""unevenly fetch the option according to the corresponding rate
:type options: list[str]
:type rate: list[num]
:rtype: str
"""
if not options or not rate or len(options) != len(rate):
return ''
num = 0
rand = random.randint(1, sum(rate))
for i in range(len(rate)):
num += rate[i]
if num >= rand:
return options[i]
return options[0] | [
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|
DLR-RM/stable-baselines3 | e9a8979022d7005560d43b7a9c1dc1ba85f7989a | stable_baselines3/common/vec_env/vec_normalize.py | python | VecNormalize.__getstate__ | (self) | return state | Gets state for pickling.
Excludes self.venv, as in general VecEnv's may not be pickleable. | Gets state for pickling. | [
"Gets",
"state",
"for",
"pickling",
"."
] | def __getstate__(self) -> Dict[str, Any]:
"""
Gets state for pickling.
Excludes self.venv, as in general VecEnv's may not be pickleable."""
state = self.__dict__.copy()
# these attributes are not pickleable
del state["venv"]
del state["class_attributes"]
# these attributes depend on the above and so we would prefer not to pickle
del state["returns"]
return state | [
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|
DamnWidget/anaconda | a9998fb362320f907d5ccbc6fcf5b62baca677c0 | anaconda_lib/helpers.py | python | valid_languages | (**kwargs) | return ['python'] + languages | Return back valid languages for anaconda plugins | Return back valid languages for anaconda plugins | [
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"back",
"valid",
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"for",
"anaconda",
"plugins"
] | def valid_languages(**kwargs):
"""Return back valid languages for anaconda plugins
"""
path = os.path.join(os.path.dirname(__file__), os.pardir, os.pardir)
languages = [
f.rsplit('_', 1)[1].lower() for f in os.listdir(path)
if f.startswith('anaconda_') and 'vagrant' not in f
]
return ['python'] + languages | [
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|
oilshell/oil | 94388e7d44a9ad879b12615f6203b38596b5a2d3 | Python-2.7.13/Lib/mailbox.py | python | _singlefileMailbox.flush | (self) | Write any pending changes to disk. | Write any pending changes to disk. | [
"Write",
"any",
"pending",
"changes",
"to",
"disk",
"."
] | def flush(self):
"""Write any pending changes to disk."""
if not self._pending:
if self._pending_sync:
# Messages have only been added, so syncing the file
# is enough.
_sync_flush(self._file)
self._pending_sync = False
return
# In order to be writing anything out at all, self._toc must
# already have been generated (and presumably has been modified
# by adding or deleting an item).
assert self._toc is not None
# Check length of self._file; if it's changed, some other process
# has modified the mailbox since we scanned it.
self._file.seek(0, 2)
cur_len = self._file.tell()
if cur_len != self._file_length:
raise ExternalClashError('Size of mailbox file changed '
'(expected %i, found %i)' %
(self._file_length, cur_len))
new_file = _create_temporary(self._path)
try:
new_toc = {}
self._pre_mailbox_hook(new_file)
for key in sorted(self._toc.keys()):
start, stop = self._toc[key]
self._file.seek(start)
self._pre_message_hook(new_file)
new_start = new_file.tell()
while True:
buffer = self._file.read(min(4096,
stop - self._file.tell()))
if buffer == '':
break
new_file.write(buffer)
new_toc[key] = (new_start, new_file.tell())
self._post_message_hook(new_file)
self._file_length = new_file.tell()
except:
new_file.close()
os.remove(new_file.name)
raise
_sync_close(new_file)
# self._file is about to get replaced, so no need to sync.
self._file.close()
# Make sure the new file's mode is the same as the old file's
mode = os.stat(self._path).st_mode
os.chmod(new_file.name, mode)
try:
os.rename(new_file.name, self._path)
except OSError, e:
if e.errno == errno.EEXIST or \
(os.name == 'os2' and e.errno == errno.EACCES):
os.remove(self._path)
os.rename(new_file.name, self._path)
else:
raise
self._file = open(self._path, 'rb+')
self._toc = new_toc
self._pending = False
self._pending_sync = False
if self._locked:
_lock_file(self._file, dotlock=False) | [
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||
Chaffelson/nipyapi | d3b186fd701ce308c2812746d98af9120955e810 | nipyapi/nifi/models/lineage_entity.py | python | LineageEntity.__repr__ | (self) | return self.to_str() | For `print` and `pprint` | For `print` and `pprint` | [
"For",
"print",
"and",
"pprint"
] | def __repr__(self):
"""
For `print` and `pprint`
"""
return self.to_str() | [
"def",
"__repr__",
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] | https://github.com/Chaffelson/nipyapi/blob/d3b186fd701ce308c2812746d98af9120955e810/nipyapi/nifi/models/lineage_entity.py#L104-L108 |
|
jliljebl/flowblade | 995313a509b80e99eb1ad550d945bdda5995093b | flowblade-trunk/Flowblade/propertyparse.py | python | _property_type | (value_str) | Gets property type from value string by trying to interpret it
as int or float, if both fail it is considered an expression. | Gets property type from value string by trying to interpret it
as int or float, if both fail it is considered an expression. | [
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"property",
"type",
"from",
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"or",
"float",
"if",
"both",
"fail",
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"is",
"considered",
"an",
"expression",
"."
] | def _property_type(value_str):
"""
Gets property type from value string by trying to interpret it
as int or float, if both fail it is considered an expression.
"""
try:
int(value_str)
return PROP_INT
except:
try:
float(value_str)
return PROP_FLOAT
except:
return PROP_EXPRESSION | [
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||
fake-name/ReadableWebProxy | ed5c7abe38706acc2684a1e6cd80242a03c5f010 | WebMirror/management/rss_parser_funcs/feed_parse_extractSnowbelldotWordpressCom.py | python | extractSnowbelldotWordpressCom | (item) | return False | Parser for 'snowbelldot.wordpress.com' | Parser for 'snowbelldot.wordpress.com' | [
"Parser",
"for",
"snowbelldot",
".",
"wordpress",
".",
"com"
] | def extractSnowbelldotWordpressCom(item):
'''
Parser for 'snowbelldot.wordpress.com'
'''
vol, chp, frag, postfix = extractVolChapterFragmentPostfix(item['title'])
if not (chp or vol) or "preview" in item['title'].lower():
return None
tagmap = [
('Cold King, the Doctor Fei Is Running Away', 'Cold King, the Doctor Fei Is Running Away', 'translated'),
('PRC', 'PRC', 'translated'),
('Loiterous', 'Loiterous', 'oel'),
]
for tagname, name, tl_type in tagmap:
if tagname in item['tags']:
return buildReleaseMessageWithType(item, name, vol, chp, frag=frag, postfix=postfix, tl_type=tl_type)
return False | [
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|
ales-tsurko/cells | 4cf7e395cd433762bea70cdc863a346f3a6fe1d0 | packaging/macos/python/lib/python3.7/asyncio/protocols.py | python | BaseProtocol.connection_made | (self, transport) | Called when a connection is made.
The argument is the transport representing the pipe connection.
To receive data, wait for data_received() calls.
When the connection is closed, connection_lost() is called. | Called when a connection is made. | [
"Called",
"when",
"a",
"connection",
"is",
"made",
"."
] | def connection_made(self, transport):
"""Called when a connection is made.
The argument is the transport representing the pipe connection.
To receive data, wait for data_received() calls.
When the connection is closed, connection_lost() is called.
""" | [
"def",
"connection_made",
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",",
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] | https://github.com/ales-tsurko/cells/blob/4cf7e395cd433762bea70cdc863a346f3a6fe1d0/packaging/macos/python/lib/python3.7/asyncio/protocols.py#L19-L25 |
||
chenjun2hao/CenterFace.pytorch | 37a351be07606136561bc56eae716f8575b61e00 | src/tools/voc_eval_lib/datasets/voc_eval.py | python | voc_eval | (detpath,
annopath,
imagesetfile,
classname,
cachedir,
ovthresh=0.5,
use_07_metric=False,
use_diff=False) | return rec, prec, ap | rec, prec, ap = voc_eval(detpath,
annopath,
imagesetfile,
classname,
[ovthresh],
[use_07_metric])
Top level function that does the PASCAL VOC evaluation.
detpath: Path to detections
detpath.format(classname) should produce the detection results file.
annopath: Path to annotations
annopath.format(imagename) should be the xml annotations file.
imagesetfile: Text file containing the list of images, one image per line.
classname: Category name (duh)
cachedir: Directory for caching the annotations
[ovthresh]: Overlap threshold (default = 0.5)
[use_07_metric]: Whether to use VOC07's 11 point AP computation
(default False) | rec, prec, ap = voc_eval(detpath,
annopath,
imagesetfile,
classname,
[ovthresh],
[use_07_metric]) | [
"rec",
"prec",
"ap",
"=",
"voc_eval",
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"annopath",
"imagesetfile",
"classname",
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"ovthresh",
"]",
"[",
"use_07_metric",
"]",
")"
] | def voc_eval(detpath,
annopath,
imagesetfile,
classname,
cachedir,
ovthresh=0.5,
use_07_metric=False,
use_diff=False):
"""rec, prec, ap = voc_eval(detpath,
annopath,
imagesetfile,
classname,
[ovthresh],
[use_07_metric])
Top level function that does the PASCAL VOC evaluation.
detpath: Path to detections
detpath.format(classname) should produce the detection results file.
annopath: Path to annotations
annopath.format(imagename) should be the xml annotations file.
imagesetfile: Text file containing the list of images, one image per line.
classname: Category name (duh)
cachedir: Directory for caching the annotations
[ovthresh]: Overlap threshold (default = 0.5)
[use_07_metric]: Whether to use VOC07's 11 point AP computation
(default False)
"""
# assumes detections are in detpath.format(classname)
# assumes annotations are in annopath.format(imagename)
# assumes imagesetfile is a text file with each line an image name
# cachedir caches the annotations in a pickle file
# first load gt
if not os.path.isdir(cachedir):
os.mkdir(cachedir)
cachefile = os.path.join(cachedir, '%s_annots.pkl' % imagesetfile)
# read list of images
with open(imagesetfile, 'r') as f:
lines = f.readlines()
imagenames = [x.strip() for x in lines]
if not os.path.isfile(cachefile):
# load annotations
recs = {}
for i, imagename in enumerate(imagenames):
recs[imagename] = parse_rec(annopath.format(imagename))
if i % 100 == 0:
print('Reading annotation for {:d}/{:d}'.format(
i + 1, len(imagenames)))
# save
print('Saving cached annotations to {:s}'.format(cachefile))
with open(cachefile, 'wb') as f:
pickle.dump(recs, f)
else:
# load
with open(cachefile, 'rb') as f:
try:
recs = pickle.load(f)
except:
recs = pickle.load(f, encoding='bytes')
# extract gt objects for this class
class_recs = {}
npos = 0
for imagename in imagenames:
R = [obj for obj in recs[imagename] if obj['name'] == classname]
bbox = np.array([x['bbox'] for x in R])
if use_diff:
difficult = np.array([False for x in R]).astype(np.bool)
else:
difficult = np.array([x['difficult'] for x in R]).astype(np.bool)
det = [False] * len(R)
npos = npos + sum(~difficult)
class_recs[imagename] = {'bbox': bbox,
'difficult': difficult,
'det': det}
# read dets
detfile = detpath.format(classname)
with open(detfile, 'r') as f:
lines = f.readlines()
splitlines = [x.strip().split(' ') for x in lines]
image_ids = [x[0] for x in splitlines]
confidence = np.array([float(x[1]) for x in splitlines])
BB = np.array([[float(z) for z in x[2:]] for x in splitlines])
nd = len(image_ids)
tp = np.zeros(nd)
fp = np.zeros(nd)
if BB.shape[0] > 0:
# sort by confidence
sorted_ind = np.argsort(-confidence)
sorted_scores = np.sort(-confidence)
BB = BB[sorted_ind, :]
image_ids = [image_ids[x] for x in sorted_ind]
# go down dets and mark TPs and FPs
for d in range(nd):
R = class_recs[image_ids[d]]
bb = BB[d, :].astype(float)
ovmax = -np.inf
BBGT = R['bbox'].astype(float)
if BBGT.size > 0:
# compute overlaps
# intersection
ixmin = np.maximum(BBGT[:, 0], bb[0])
iymin = np.maximum(BBGT[:, 1], bb[1])
ixmax = np.minimum(BBGT[:, 2], bb[2])
iymax = np.minimum(BBGT[:, 3], bb[3])
iw = np.maximum(ixmax - ixmin + 1., 0.)
ih = np.maximum(iymax - iymin + 1., 0.)
inters = iw * ih
# union
uni = ((bb[2] - bb[0] + 1.) * (bb[3] - bb[1] + 1.) +
(BBGT[:, 2] - BBGT[:, 0] + 1.) *
(BBGT[:, 3] - BBGT[:, 1] + 1.) - inters)
overlaps = inters / uni
ovmax = np.max(overlaps)
jmax = np.argmax(overlaps)
if ovmax > ovthresh:
if not R['difficult'][jmax]:
if not R['det'][jmax]:
tp[d] = 1.
R['det'][jmax] = 1
else:
fp[d] = 1.
else:
fp[d] = 1.
# compute precision recall
fp = np.cumsum(fp)
tp = np.cumsum(tp)
rec = tp / float(npos)
# avoid divide by zero in case the first detection matches a difficult
# ground truth
prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)
ap = voc_ap(rec, prec, use_07_metric)
return rec, prec, ap | [
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] | https://github.com/chenjun2hao/CenterFace.pytorch/blob/37a351be07606136561bc56eae716f8575b61e00/src/tools/voc_eval_lib/datasets/voc_eval.py#L70-L215 |
|
iclavera/learning_to_adapt | bd7d99ba402521c96631e7d09714128f549db0f1 | learning_to_adapt/mujoco_py/mjtypes.py | python | MjModelWrapper.light_diffuse | (self, value) | [] | def light_diffuse(self, value):
val_ptr = np.array(value, dtype=np.float64).ctypes.data_as(POINTER(c_float))
memmove(self._wrapped.contents.light_diffuse, val_ptr, self.nlight*3 * sizeof(c_float)) | [
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||||
codepr/creak | 52b0d74c3f77dad45e97dd7aa22976b83a0d3676 | creak.py | python | get_mitm | (parsed_args) | return (args, changed, original_mac_addr,
cmitm.PcapMitm(dev, utils.parse_mac(mac_addr), args.source,
args.target, debug, verbose)) | create an object of type Mitm based on arguments received | create an object of type Mitm based on arguments received | [
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"""
create an object of type Mitm based on arguments received
"""
args = parsed_args
if not args.dev:
sys.exit(sys.argv[0] + ' -h for help\n[!] Must specify interface')
dev = "%s" % "','".join(args.dev)
original_mac_addr = utils.get_mac_by_dev(dev)
mac_addr, changed = original_mac_addr, False
if not args.source:
try:
args.source = utils.get_default_gateway_linux()
except OSError:
args.source = raw_input('[!] Unable to retrieve default gateway, please specify one: ')
if not utils.is_ipv4(args.source):
exit('[!] Unable to retrieve default gateway, please specify one using -s option')
else:
pass
if not args.target:
args.target = raw_input('[?] No target address specified, please insert one: ')
if not utils.is_ipv4(args.target):
exit('[!] Must specify at least one target address')
else:
if len(args.target) == 1:
args.target = ''.join(args.target)
conf = ConfigParser.ConfigParser()
conf.read('./creak/config')
verbose = conf.getboolean('output', 'VERBOSE')
debug = conf.getboolean('output', 'DEBUG')
if args.verbosity:
verbose = True
if args.debug:
debug = True
if args.spoof is True:
choice = raw_input('[+] In order to change MAC address ' + G + dev + W
+ ' must be temporary put down. Proceed?[y/n] ')
if choice == 'y':
if not args.macaddr and not args.manufacturer:
mac_addr = utils.fake_mac_address([], 1)
elif args.macaddr and not args.manufacturer:
if utils.parse_mac(args.macaddr) != utils.parse_mac(original_mac_addr):
mac_addr = utils.fake_mac_address(utils.mac_to_hex(args.macaddr))
elif args.manufacturer:
macs = utils.get_manufacturer(args.manufacturer)
mac_addr = utils.fake_mac_address(utils.mac_to_hex(random.choice(macs)))
try:
utils.change_mac(dev, mac_addr)
changed = True
except OSError:
pass
print("[+] Waiting for wireless reactivation..")
if args.mode == 1 or args.mode == 2:
time.sleep(10)
else:
time.sleep(4)
# no spoof but set mac address anyway
elif args.macaddr:
mac_addr = args.macaddr
print("[+] Using " + G + mac_addr + W + " MAC address\n"
"[+] Set " + G + args.source + W + " as default gateway")
if conf.get('output', 'ENGINE').lower() == 'scapy':
return (args, changed, original_mac_addr,
cmitm.ScapyMitm(dev, utils.parse_mac(mac_addr), args.source,
args.target, debug, verbose))
return (args, changed, original_mac_addr,
cmitm.PcapMitm(dev, utils.parse_mac(mac_addr), args.source,
args.target, debug, verbose)) | [
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|
angr/angr | 4b04d56ace135018083d36d9083805be8146688b | angr/analyses/vtable.py | python | VtableFinder.is_cross_referenced | (self, addr) | return addr in self.project.kb.xrefs.xrefs_by_dst | [] | def is_cross_referenced(self, addr):
return addr in self.project.kb.xrefs.xrefs_by_dst | [
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|||
sametmax/Django--an-app-at-a-time | 99eddf12ead76e6dfbeb09ce0bae61e282e22f8a | ignore_this_directory/django/db/backends/base/operations.py | python | BaseDatabaseOperations.max_name_length | (self) | return None | Return the maximum length of table and column names, or None if there
is no limit. | Return the maximum length of table and column names, or None if there
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Return the maximum length of table and column names, or None if there
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|
kuri65536/python-for-android | 26402a08fc46b09ef94e8d7a6bbc3a54ff9d0891 | python3-alpha/python-libs/pyxmpp2/ext/vcard.py | python | VCardName.as_xml | (self,parent) | return n | Create vcard-tmp XML representation of the field.
:Parameters:
- `parent`: parent node for the element
:Types:
- `parent`: `libxml2.xmlNode`
:return: xml node with the field data.
:returntype: `libxml2.xmlNode` | Create vcard-tmp XML representation of the field. | [
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] | def as_xml(self,parent):
"""Create vcard-tmp XML representation of the field.
:Parameters:
- `parent`: parent node for the element
:Types:
- `parent`: `libxml2.xmlNode`
:return: xml node with the field data.
:returntype: `libxml2.xmlNode`"""
n=parent.newChild(None,"N",None)
n.newTextChild(None,"FAMILY",to_utf8(self.family))
n.newTextChild(None,"GIVEN",to_utf8(self.given))
n.newTextChild(None,"MIDDLE",to_utf8(self.middle))
n.newTextChild(None,"PREFIX",to_utf8(self.prefix))
n.newTextChild(None,"SUFFIX",to_utf8(self.suffix))
return n | [
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|
sagemath/sage | f9b2db94f675ff16963ccdefba4f1a3393b3fe0d | src/sage/modular/hecke/module.py | python | HeckeModule_free_module._repr_ | (self) | return repr(type(self)) | r"""
EXAMPLES::
sage: M = sage.modular.hecke.module.HeckeModule_free_module(QQ, 12, -4); M
<class 'sage.modular.hecke.module.HeckeModule_free_module_with_category'>
.. TODO::
Implement a nicer repr, or implement the methods required
by :class:`ModulesWithBasis` to benefit from
:meth:`ModulesWithBasis.ParentMethods._repr_`. | r""" | [
"r"
] | def _repr_(self):
r"""
EXAMPLES::
sage: M = sage.modular.hecke.module.HeckeModule_free_module(QQ, 12, -4); M
<class 'sage.modular.hecke.module.HeckeModule_free_module_with_category'>
.. TODO::
Implement a nicer repr, or implement the methods required
by :class:`ModulesWithBasis` to benefit from
:meth:`ModulesWithBasis.ParentMethods._repr_`.
"""
return repr(type(self)) | [
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|
mchristopher/PokemonGo-DesktopMap | ec37575f2776ee7d64456e2a1f6b6b78830b4fe0 | app/pywin/Lib/random.py | python | Random.jumpahead | (self, n) | Change the internal state to one that is likely far away
from the current state. This method will not be in Py3.x,
so it is better to simply reseed. | Change the internal state to one that is likely far away
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"""Change the internal state to one that is likely far away
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"""
# The super.jumpahead() method uses shuffling to change state,
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s = repr(n) + repr(self.getstate())
n = int(_hashlib.new('sha512', s).hexdigest(), 16)
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||
OpenEndedGroup/Field | 4f7c8edfb01bb0ccc927b78d3c500f018a4ae37c | Contents/lib/python/mailbox.py | python | MH.unlock | (self) | Unlock the mailbox if it is locked. | Unlock the mailbox if it is locked. | [
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"""Unlock the mailbox if it is locked."""
if self._locked:
_unlock_file(self._file)
_sync_close(self._file)
self._file.close()
del self._file
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||
Udayraj123/OMRChecker | 4f11cf4a30c9f6c1cf780f300837d50f7db1fda7 | utils.py | python | adjust_gamma | (image, gamma=1.0) | return cv2.LUT(image, table) | [] | def adjust_gamma(image, gamma=1.0):
# build a lookup table mapping the pixel values [0, 255] to
# their adjusted gamma values
invGamma = 1.0 / gamma
table = np.array([((i / 255.0) ** invGamma) * 255
for i in np.arange(0, 256)]).astype("uint8")
# apply gamma correction using the lookup table
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|||
tomplus/kubernetes_asyncio | f028cc793e3a2c519be6a52a49fb77ff0b014c9b | kubernetes_asyncio/client/models/v1_aws_elastic_block_store_volume_source.py | python | V1AWSElasticBlockStoreVolumeSource.read_only | (self) | return self._read_only | Gets the read_only of this V1AWSElasticBlockStoreVolumeSource. # noqa: E501
Specify \"true\" to force and set the ReadOnly property in VolumeMounts to \"true\". If omitted, the default is \"false\". More info: https://kubernetes.io/docs/concepts/storage/volumes#awselasticblockstore # noqa: E501
:return: The read_only of this V1AWSElasticBlockStoreVolumeSource. # noqa: E501
:rtype: bool | Gets the read_only of this V1AWSElasticBlockStoreVolumeSource. # noqa: E501 | [
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Specify \"true\" to force and set the ReadOnly property in VolumeMounts to \"true\". If omitted, the default is \"false\". More info: https://kubernetes.io/docs/concepts/storage/volumes#awselasticblockstore # noqa: E501
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return self._read_only | [
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